Data Analytics for Trajectory Selection and Preference-Model Extrapolation in the European Airspace

Carlo Lancia (), Luigi De Giovanni () and Guglielmo Lulli ()
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Carlo Lancia: Mathematical Institute Leiden University
Luigi De Giovanni: Università degli studi di Padova
Guglielmo Lulli: Lancaster University Management School

A chapter in Operations Research Proceedings 2018, 2019, pp 563-570 from Springer

Abstract: Abstract Representing airspace users’ preferences in Air Traffic Flow Management (ATFM) mathematical models is becoming of high relevance. ATFM models aim to reduce congestion (en-route and at both departure and destination airports) and maximize the Air Traffic Management (ATM) system efficiency by determining the best trajectory for each aircraft. In this framework, the a-priori selection of possible alternative trajectories for each flight plays a crucial role. In this work, we analyze initial trajectories queried from Eurocontrol DDR2 data source. Clustering trajectories yields groups that are homogeneous with respect to known (geometry of the trajectory, speed) and partially known or unknown factors (en-route charges, fuel consumption, weather, etc.). Associations between grouped trajectories and potential choice-determinants are successively explored and evaluated, and the predictive value of the determinants is finally validated. For a given origin-destination pair, this ultimately leads to determining a set of flight trajectories and information on related airspace users’ preferences.

Keywords: Air traffic flow management; Data analytics; Mathematical models; Airspace users’ preferences (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-030-18500-8_70

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